The present invention relates to data processing systems, and in particular, to systems and methods for processing data, including auto-ID data, using rule engines.
Auto-ID systems are highly distributed systems that include information about themselves. These devices are sometimes referred to as “smart item devices” and are referred to herein as “auto-ID devices.” Auto-ID systems are used, for example, to identify or otherwise obtain information about individual products or items that are to be manufactured, bought or sold, transported, or otherwise used in commerce. For example, information regarding a physical object, such as a pallet on a shipping dock, may be stored in association with an electronic identifier (e.g., a tag ID) or a sensor that is affixed to the pallet or to goods on the pallet. Then, some sort of auto-ID device, such as a reader or sensor, may be used to identify the physical object or the temperature, for example, of the object by accessing the identifier and/or the sensor. Accordingly, auto-ID devices may generate auto-ID data corresponding to the products or objects that they are associated with. As used herein, auto-ID data are data that identifies or describes a real world object (e.g., RFID data) or data that describes the physical environment of a real world object (e.g., sensor data). The accessed information may be sent to a computer system for storage and processing. For example, a brand name of the object and/or the temperature of the object may be read, transmitted to a computer system, stored, and processed.
Radio frequency identification (“RFID”) tags that contain information about the object they are associated with provide a simple form of a smart item device. RFID tags typically combine a modest storage capacity with a means of wirelessly communicating stored information like an electronic product code (EPC) to an RFID reader. In a supply chain management context, an object to be tagged is usually a pallet, a case or even a single sales item. Passive RFID tags require no on-board battery and can be read from a distance ranging from a few centimeters to a few meters. Active tags, on the other hand, come with an on-board battery, which provides larger read ranges and memory sizes but also higher unit cost and size and a limited lifespan of typically 3-5 years. Another example of a smart device in this context is an environmental sensor, such as a temperature or humidity sensor, which can provide a more complete picture of a tracked object and its physical environment.
Through automatic, real-time object tracking, smart item technology can provide companies with more accurate data about their business operations in a more timely fashion, as well as helping to streamline and automate the operations themselves. This leads to cost reduction and additional business benefits like increased asset visibility, improved responsiveness, and even extended business opportunities. However, bridging the gap between the physical and the digital world requires a flexible and scalable system architecture to integrate automatic data acquisition with existing business processes.
FIG. 1 illustrates a typical auto-ID system 100. Auto-ID system 100 can be divided into a device layer 101, a device operation layer 102, a bridging layer 103 (e.g., a business process bridging layer), and an application layer 104. Device layer 101 may include a variety of different auto-ID devices, such as RFID tags 110 and 112 or sensors 111, for example. Different types of RFID or sensor devices can be supported via a hardware-independent low-level interface. The device layer consists of the basic operations for reading and writing data and a publish/subscribe interface to report observation events. By implementing an application program interface (“API”), different kinds of “smart item” devices can be deployed within the Auto-ID infrastructure. Besides RFID readers, these devices can include environmental sensors, or programmable logic controller (“PLC”) devices. Device operation layer 102 coordinates multiple devices. It may also provide functionality to filter, condense, aggregate, and adjust received sensor data before passing it on to the next layer. This layer is formed by one or more Device Controllers (“DC”) 120 and 121. The bridging layer 103 associates incoming observation messages with existing business processes. At this layer, status and history information of tracked objects is maintained. This information may include object location, aggregation information, and information about the environment of a tagged object. This layer may include an “Auto-ID Node” (“AIN”) 130 including a complex and powerful data processing software component. The bridging layer may further include a storage facility, such as a database repository 132, for storing auto-ID data and an auto-ID administrator component 131 for managing the auto-ID node. Finally, application layer 104 supports computer applications that use the incoming auto-ID information, such as business processes of enterprise applications 140 including a Supply Chain Management (SCM), a Customer Relationship Management (CRM), or an Asset Management application, for example. Application layer 104 may include databases 141, such as a data warehouse, and a portal 142 for accessing the applications.
FIG. 1 illustrates a typical auto-ID system 100. FIG. 2 illustrates an example use of an auto-ID system. Effective deployment of RFID technology can make it easier for business partners to acquire and share real-time data about goods and conduct transactions electronically. If a supplier ships 12 pallets of goods to a retail distribution center, passive RFID tags may be attached to them. At layer 250, an application may create a delivery document at step 201 that includes an Advanced Shipping Notice (“ASN”) and electronic product codes (“EPC”). The RFID tags on the pallets may be scanned automatically as they leave the supplier's loading dock. When the truck door is closed and sealed with an RFID bolt seal, scanning the bolt seal could trigger the supplier's internal system to send the delivery document to the retailers RFID system automatically at 202.
On the other end, the retailer's auto-ID node 260 receives the ASN and is able to read and store it at 203. The retailer can then use the ASN to verify the accuracy of the shipment once it arrives and is unloaded from the truck. When the retailer scans the RFID tags on the 12 arriving pallets at 204 and 207 using readers 270, the raw data is transmitted by the device controllers (“DC”) to the auto-ID node 260. The auto-ID node compares the received information with the information in the ASN at 205 and 208. If the information matches, the retailer's auto-ID node can automatically confirm that the shipment arrived by generating and transmitting a report 233 to a back-end application at 209. This may automatically trigger a message to the supplier to generate an electronic invoice, for example. One or more of the retailer's back-end application may continue execution at 210 based on the information from the auto-ID node. If the information on the tags and the ASN do not match up, the receiver's system may request that the supplier's system confirm the shipment. From FIGS. 1 and 2 it can be seen that an almost infinite number of business process rules could be applied to an auto-ID system taking advantage of the availability of auto-ID data.
However, one problem with existing auto-ID systems is that a potentially vast amount of information may be acquired. For example, a certain number of readers may access auto-ID data from a number of tags or sensors. Moreover, the desired methods (e.g., business rules) for processing such information may change over time. Typically, raw data processing is carried out on the Auto-ID node 103 in the bridging layer 103 in the form of business rules that are executed in a monolithic AIN component. However, it is generally desirable to develop a more distributed and flexible architecture which includes moving part of the business logic closer to the point of observation and reacting locally for better system scalability and throughput. However, lower level devices typically have limited memory and computation power. Therefore, the processing of complex business rules in the form they are currently expressed and executed on the AIN is not always possible on the smart devices, like RFID or sensor nodes.
Thus, there is a need for improved auto-ID data processing. The present invention solves these and other problems by providing improved systems and methods for processing auto-ID data.